Data visualization could use tighter integrations

The other day, I was playing around in SQL and Tableau to visualize some data for a project I was doing. As we know, these two platforms go hand in hand for data analysts and anyone who has an analytics-based position. The ability to add the data sets from SQL or as CSVs is seamless, and you can use SQL-like structure to filter within the interface. Most data analysts need Python as well, but what I find funny is that many professionals have told me that Power BI is necessary to visualize Python scripts. It makes sense. Power BI allows you to run scripts inside the platform. You can use it for data cleaning, data transformation, and advanced modeling. This always made me wonder why the two were separate. If being a data analyst required different coding platforms to understand, would a visualization tool be best to integrate between different coding platforms? Think about it, SQL for querying, Python for modeling and automation, R for Statistical analysis, maybe even Java for some enterprise environments. From a systems perspective, it seems inefficient. I understand filtering becomes more difficult because the syntax and order of filtering are different from one another. Sometimes, even Tableau has filtering that orders differently from SQL, which you need to get used to. Regardless, I think bringing tools together into one platform would centralize the final data-backed outcomes. Managers, board of directors, and your CEO are not going to care what you did to visualize your ideas and data. They care about clarity, speed, and clear-cut results that can be used for business insights. Some of these findings could be siloed or require switching between them. The future of analytics tools relies on a tighter integration. Not just connecting languages, but harmonizing them. It can surely streamline workflows and reduce cognitive workload.